System and Method for Reporting Website Activity Based on Inferred Attribution Methodology

ABSTRACT

A method and system for reporting website activity. According to an example embodiment, the system receives event-level data representing visitor activity on a client website, infers attribution of one or more metrics to at least one navigation entity based on the visitor activity, and provides reports based on the inferred attribution.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.10/245,579, filed Sep. 18, 2002, which is related to U.S. patentapplication Ser. No. 10/245,580, now U.S. Pat. No. 7,085,682, thedisclosure of each of which is hereby incorporated by reference in itsentirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor patent disclosure as it appears in the Patent and Trademark Officepatent file or records, but otherwise reserves all copyright rightswhatsoever.

BACKGROUND OF THE INVENTION

The increase in electronic commerce over the Internet has resulted in agrowing demand for websites to track their online customers' behaviorand activity while at their sites. Tracking this activity enables thewebsite to better understand their customers, which provides insightinto ways in which the websites' service and/or offerings can beimproved. Websites can track their information on their own, but largersites enlist the aid of third party application software or a thirdparty application service provider (“ASP”) to do the work for them.

Client websites place great value on the ability of an ASP, forinstance, to report on overall website metrics, such as revenue orcartings (i.e., the action of a visitor placing a product in a cart),and on what is driving the metrics on the client's website. Current ASPsystems attribute overall website metrics to specific navigationentities, like hyperlinks, pages and sections (i.e., collections ofpages pertaining to a specific grouping of products, like “sportsequipment” or “laptop accessories”), which enables clients to gain anunderstanding of which navigation entities have significant impact onthose metrics. However, in order for ASPs to break down the overallmetrics according to navigation entity, the ASP requires knowledge ofthe mapping of the metrics to the navigation entities prior toattribution taking place.

To implement this attribution methodology, an ASP system may require, asan initial setup matter, a client web site map that details whichnavigation entity will receive attribution of which metric prior to thesystem being run. For example, if a client web site showcases a specificdigital video disc (“DVD”) on its home page, there may be an entry inthe corresponding web site map specifying that revenue resulting fromthe purchase of that DVD be attributed to the home page, therebycrediting the home page for directing the visitor to the DVD purchase.When the ASP sees that a visitor bought the DVD, the ASP system consultsthe map and attributes the corresponding revenue to the home page.

A major drawback to this mapping process is that there is an up fronteffort required to initially map all relevant metrics to theircorresponding navigation entities. Additionally, if the client web sitemakes any changes to a mapped navigation entity on its site, the mapheld in the ASP system must be updated to reflect those changes in orderfor the metrics to be properly attributed to the changed navigationentities. This increases the client's ongoing effort to maintain the ASPsystem.

Some ASPs may eliminate the need for the web site map by requiringattribution information to be provided on each relevant web page, and tobe carried forward (e.g., via session variables or query strings in theuniform resource locator (“URL”)) through a visitor's navigation pathduring a session. For example, if a client web site showcases a specificDVD on its home page, attribution information on the home page (such as“apply revenue to home page”) may follow the visitor through to checkout. Thus, if a visitor ends up buying the DVD, the ASP system will seethe carried through attribution information when the visitor pays forthe DVD, and know to attribute that revenue to the home page.

However, every time a change is made to a navigation entity under thisapproach, client effort is still required to change the attributioninformation that is held on that navigation entity and to be carriedforward during a session.

Accordingly, there is a need in the art for a low-maintenance system andmethod for enabling attribution without additional client effort when anavigation entity is added, removed or changed.

SUMMARY OF THE INVENTION

The present invention is directed to a system and method for analyzingonline customer activity at a website in a cost-effective and efficientmanner. Efficient data collection, processing, attribution and reportpresentation processes enable client websites to quickly access andunderstand the interaction between site traffic and transactions, andthose factors that drive each transaction.

According to an example embodiment, the system receives event-level datarepresenting visitor activity on a client website, infers attribution ofone or more metrics to at least one navigation entity based on thevisitor activity, and provides reports based on the inferredattribution.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that depicts a process for providing a reporton website activity based on an inferred attribution methodology inaccordance with an embodiment of the present invention.

FIG. 2 is a block diagram that depicts a user computing device inaccordance with an embodiment of the present invention.

FIG. 3 is a block diagram that depicts a network architecture for ananalysis system in accordance with an embodiment of the presentinvention.

FIG. 4 is a block diagram that depicts a traffic-driving hyperlink inaccordance with an embodiment of the present invention.

FIG. 5 is a block diagram that depicts a product hyperlink in accordancewith an embodiment of the present invention.

FIG. 6 is a block diagram that depicts generic navigation entities inaccordance with an embodiment of the present invention.

FIG. 7 is a screen shot of a Visitor Section Navigation Paths analysispage in accordance with an embodiment of the present invention.

FIG. 8 is a screen shot of an Shelf Space Analysis page in accordancewith an embodiment of the present invention.

FIG. 9 is a screen shot of a Product Placement Analysis page inaccordance with an embodiment of the present invention.

DETAILED DESCRIPTION Overview

FIG. 1 provides an overview of a process and system according to anembodiment of the present invention. The system receives event-leveldata, representing specific events that describe a customer's presenceand/or activity through navigation entities at a client website, such asclicking on a specific web page or buying a specific product. Uponreceipt of the event-level data, the system sorts the event-level databy visitor and time received (step 100), in order to group the activityby visitor sessions. The system next performs an inferred attributionanalysis (step 110) on the event-level data, which attributes metrics,such as revenue or cartings, to at least one navigation entity based onthe visitor activity and not explicit attribution information from theevent-level data. The system then generates page-pairing data (step 120)to be used in providing a report to the client (step 130).

Architecture

FIG. 2 is a block diagram depicting the internal structure of usercomputing device 200 in accordance with an embodiment of the presentinvention. User computing device 200 may be a personal computer,handheld personal digital assistant (“PDA”), or any other type ofmicroprocessor-based device. User computing device 200 may include aprocessor 210, input device 220, output device 230, storage device 240,web browser 250, and communication device 260.

Input device 220 may include a keyboard, mouse, pen-operated touchscreen, voice-recognition device, or any other device that providesinput from a user. Output device 230 may include a monitor, printer,disk drive, speakers, or any other device that provides tangible outputto user.

Storage device 240 may include volatile and nonvolatile data storage.Volatile data storage includes random-access memory (“RAM”), a cache, orany storage medium that temporarily holds data while being processed;nonvolatile data storage includes a hard drive, compact disc read-onlymemory (“CD-ROM”) drive, tape drive, removable storage disk, or anyother non-temporary storage medium. Communication device 260 may includea modem, network interface card, or any other device capable oftransmitting and receiving signals over a network.

Web browser 250, which may be stored in storage device 240 and executedby processor 210, may include INTERNET EXPLORER® web browser byMicrosoft Corp. or the COMMUNICATOR® web browser by NetscapeCommunications Corp., or any other software program that displays datafrom a web server to a user via output device 230. One skilled in theart would appreciate that the components of user computing device 200may also be connected wirelessly, possibly through an infraredconnection.

FIG. 3 is a block diagram depicting a network architecture for ananalysis system in accordance with an embodiment of the presentinvention. According to one particular embodiment, when customer 300visits the website of client 320, user computing device 200 sends andreceives via web browser 250 HTTP (“Hypertext Transport Protocol”)requests (or any similar protocol requests) to and from web server 330via network link 315 a, computer network 310, and network link 315 b. Ascustomer 300 proceeds through client 320's website, web server 330 sendsinformation about customer 300's online activity to application server350 of analytics system 340 (via network link 315 b, computer network310 and network line 315 c). After receiving this information (e.g., theevent-level data), application server 350 employs application software360 to perform the inferred attribution analysis and provide reportsbased on that analysis. Throughout this process, transition tablesholding resultant data used for providing the reports are generated andstored in database 370. Client 320 may view and interact with thegenerated report through client 320's web browser (not shown).

Network link 315 may include telephone lines, digital subscriber line(“DSL”), cable networks, T1 or T3 lines, wireless network connections,or any other arrangement that provides a medium for the transmission andreception of computer network signals. Computer network 310 may includea wide-area network (“WAN”), such as the Internet, and a local-areanetwork (“LAN”), such as an intranet or extranet. It should be notedthat, technically, user computing device 200, network link 315, webserver 330, application server 350 and any intermediate networkcomponents, such as Internet service providers and routers (not shown),are also part of computer network 310 because of their connectivity.

Computer network 310 may implement any number of communicationsprotocols, including TCP/IP (“Transmission Control Protocol/InternetProtocol”). The communication between user computing device (“UCD”) 200,web server 330 and application server 350 may be secured by any Internetsecurity protocol, such as SSL (“Secured Sockets Layer”).

Web server 330 and application server 350 each include a processor andmemory for executing program instructions, as well as a networkinterface (not shown), and may include a collection of servers workingin tandem to distribute the network functionality and load. In oneparticular embodiment, application server 320 may include a combinationof enterprise servers such as a web application server, a web userinterface server and a database server, all of which could bemanufactured by Sun Microsystems, Inc. The web server (of analyticssystem 340 as well as web server 330) could run an HTTP server programin one embodiment, such as Apache®, as a process under an operatingsystem such as UNIX® (or any variant thereof). Database 370 may be partof a relational database program, such as MySQL® or Oracle®, that may berun as a process by a database server within the UNIX® operating system,for example.

Application software 330 may take the form of custom-written programsand libraries that run, either interpreted or compiled, in part as aresult of HTTP requests received by application server 320. Theseprograms may be written in any programming language, such as C, C++, orPERL (“Practical Extraction and Reporting Language”), and they maygenerate an HTML (“Hypertext Markup Language”) client interface ofanalytics system 340. Application software 360 may be built on aweb-based enterprise application platform, such as J2EE® (“Java 2Platform, Enterprise Edition”).

Tagging

In one example embodiment of the present invention, Web server 330tracks and sends customer 300's online activity to application server350 through the use of IMG tags (“event tags”) placed on certain pagesof client 320's website. The IMG tag is an HTML image request for a 1×1pixel GIF from application server 350, and includes key-value pairs thatare used to pass the event-level data to application server 350.

For example, each event tag may include key-value pairs to capture dataabout such events as identification of the client site hosting thevisitor, the web pages that the visitors (e.g., customer 300) view, theweb pages where the visitors place products in their shopping carts, andwhere the visitors came from before they viewed a tagged web page. Thefollowing is an example such an event tag (with key-value pairshighlighted in bold):

<img src=‘http://client.rpts.net/activity;src=12;ord=12121212?;pgnm=Home+Page;sect=Home+Page;pgurl=http://www.client.com/Default.asp?;ref=http://search.yahoo.com/bin/search?p=client.com’>

(Note that, for readability purposes, the above example code has leftout URL encoding that may be applied to non-alphanumeric characters in aworking embodiment.) In the above tag, “src” is the key for the clientsite ID (with value “12”), “ord” is the key for a random number used todefeat inadvertent duplicate page loads (with value “12121212”), “pgnm”is the key for the name of the current web page, provided by client 320(with value “Home+Page”), “sect” is the key for the name of the websitesection to which the current web page belongs, also provided by client320 (with value “Home+Page”), “pgurl” is the key for the URL of thecurrent web page (having value “http://www.client.com/Default.asp?”),and “ref” is the key for the referring URL of the current web page (withvalue “http://search.yahoo.com/bin/search?p=client.com”).

Of course, additional data may be supplied using additional keys. Otherkey-value pairs may be utilized to provide information about a productclicked on by a visitor (via a product identifier value), a productplaced into a shopping cart, a product converted (i.e., purchased afterbeing placed in a shopping cart), visitor segment membership and custominformation. Client 320 may upload a product information file (e.g.,including product identifier, name and category) to application server350 so that application software 360 can match a product identifier inthe IMG tag with the actual product information for reporting purposes.

Inferred Attribution Methodology

The event information automatically sent to application server 350 fromweb server 330 through the event tag functionality (i.e., theevent-level data) may be collected in a log file by application server350. When the time arrives to analyze the event-level data (e.g., once aday), application software 360 first sorts the events from the log fileof the event-level data by visitor and time received by analytics system340 (step 100). This sort causes all events associated with each visitorduring each visitor's session to be listed in chronological order,grouped by visitor. A visitor's session may be defined as any sequenceof events that occur within a certain time (e.g., 30 minutes) of oneanother, and ending after a completed purchase. Further, applicationsoftware 360 may rely on its own “cookie” information, passed toapplication server 350 from each visitor's web browser 250 during anevent tag request, in order to determine which events have originatedfrom the same visitor (assuming, of course, that the visitor has notopted out of client 320's analytics system 340 cookie, is not behind aproxy server which automatically blocks cookies, or has not disabledreceiving cookies via the browser's settings).

According to an example embodiment of the present invention, any one ormore of the following five metrics may be attributed by analytics system340:

-   -   1. Clicks—the number of times a hyperlink is clicked on a given        page    -   2. Page Views—the number of page loads of any individual page        (product detail page or section page) or a given section    -   3. Product Cartings—the number of units of a given product that        is added to a visitor shopping cart irrespective of the eventual        purchase of that product    -   4. Product Purchases—the number of units of a given product that        is purchased    -   5. Revenue—the total revenue for the product purchases

The following table specifies which of the above-referenced examplemetrics are useful to one or more example navigation entities:

Metric Product Page Product pur- Rev- Navigation Entity Clicks Viewscartings chases enue Product Detail Pages No Yes Yes Yes Yes SectionPages No Yes Yes Yes Yes Sections No Yes Yes Yes Yes Traffic-drivinghyperlinks Yes No Yes Yes Yes Product hyperlinks Yes No Yes Yes Yes

The following defines three navigation entities according to an inferredattribution methodology (step 120):

-   -   Pages—standard content delivered into one individual web browser        window. The two types of pages may be:        -   Product Detail Pages—pages that have detailed product            information, and may be identified by the presence of a            “prodinfo=######” key-value pair in an event tag. Since            product detail pages have no pre-defined section membership,            analytics system 340 will attribute activity from the            product detail page to the section of the last section page            seen by the visitor before the product detail page.        -   Section Pages—pages that do not have detailed product            information for one product alone, and may be all pages that            do not have a “prodinfo” key-value pair in the event tag            (including a shopping cart page).    -   Sections—a collection of any combination of pages (either        section pages or product detail pages). Again, product detail        pages have no pre-defined section membership, but analytics        system 340 will attribute activity from the product detail page        to the section of the last section page seen by the visitor        before the product detail page.    -   Hyperlinks—are standard, clickable text or images (including        pictures and buttons) that bring the website visitor to another        distinct page within the website.        -   Traffic-driving hyperlinks—hyperlinks that take the visitor            from any page to a section page. These can be thought of as            signs in a physical retail store that tell customers where            to go to find other similar or different types of products            or other information.        -   Example: As shown in FIG. 4, presuming that a visitor starts            on Page P (which can be a section page or product detail            page), analytics system 340 assumes that a traffic-driving            hyperlink exists on Page P which is the last page seen            before a section page in a sequence of pages for a given            cookie within a given session.        -   Product hyperlinks—hyperlinks that take the visitor from any            page to a product detail page. These can be thought of as            the action of taking a product off the shelf (section page)            and looking at it.        -   Example: As shown in FIG. 5, presuming that a visitor starts            on Page P (which can be a section page or product detail            page), analytics system 340 assumes that a product hyperlink            exists on Page P which is the last page seen before a            product detail page in sequence of pages for a given cookie            within a given session.

Since an inferred attribution methodology is a complex method ofattributing metrics to the navigation entities, the following tablesshow the attribution with a simple and defined example which can begeneralized to any website. As shown in FIG. 6, consider the followingwebsite for which:

-   -   There are only 6 uniquely distinct pages    -   There are only 2 products on sale (Product X and Product Y)    -   There are only 2 sections on the site (Section A and Commerce        Section)    -   Product X costs $1 and Product Y costs $5    -   The arrows indicate the only navigation flow possible    -   The visitor can leave the website off of any of the 6 pages

Note that any event is attributable to at least one navigation entityand the sum of all navigation entities may be more than the total numberof the occurrences of that event on the website.

According to the following visitor's navigation path:

Page Visitor Action Taken on the Page 600 Click on “Product X hyperlink”610 Click on “Add Product X to Cart hyperlink” 640 Visitor confirmpurchase of Product X and submits all relevant billing and shippinginformation 650 No action takenthe following attribution may be inferred:

Metric Product Page Product pur- Rev- Navigation Entity Clicks Viewscartings chases enue Product Detail Pages No Product X Detail Page 1 0 0$0 Product Y Detail Page 0 0 0 $0 Section Pages No Section A: Page 1 1 1Prod 1 Prod $1 X X Section A: Page 2 0 0 0 $0 Commerce: Shopping Cart 10 0 $0 page Commerce: “Thank You” 1 0 0 $0 page Sections No Section A 21 Prod 1 Prod $1 X X Commerce Section 2 0 0 $0 Traffic-drivinghyperlinks No 600: Page 2 of Section A 0 0 0 $0 610: Add Product X toCart 0 0 0 $0 620: Add Product Y to Cart 0 0 0 $0 630: Add Product X toCart 0 0 0 $0 630: Add Product Y to Cart 0 0 0 $0 Product hyperlinks No600: Product X 1 1 1 $1 600: Product Y 0 0 0 $0 630: Product X(implicit) 0 0 0 $0 630: Product Y (implicit) 0 0 0 $0

According to the following visitor's navigation path:

Page Visitor Action Taken on the Page 600 Click on “Product Y hyperlink”620 Click on “Add Product Y to Cart hyperlink” 640 Visitor confirmpurchase of Product Y and submits all relevant billing and shippinginformation 650 No action takenthe following attribution may be inferred:

Metric Product Page Product pur- Rev- Navigation Entity Clicks Viewscartings chases enue Product Detail Pages No Product X Detail Page 0 0 0$0 Product Y Detail Page 1 0 0 $0 Section Pages No Section A: Page 1 1 1Prod 1 Prod $5 Y Y Section A: Page 2 0 0 0 $0 Commerce: Shopping Cart 10 0 $0 page Commerce: “Thank You” 1 0 0 $0 page Sections No Section A 21 Prod 1 Prod $5 Y Y Commerce Section 2 0 0 $0 Traffic-drivinghyperlinks No 600: Page 2 of Section A 0 0 0 $0 610: Add Product X toCart 0 0 0 $0 620: Add Product Y to Cart 0 0 0 $0 630: Add Product X toCart 0 0 0 $0 630: Add Product Y to Cart 0 0 0 $0 Product hyperlinks No600: Product X 0 0 0 $0 600: Product Y 1 1 1 $5 630: Product X(implicit) 0 0 0 $0 630: Product Y (implicit) 0 0 0 $0

According to the following visitor's navigation path:

Page Visitor Action Taken on the Page 600 Click on “Page 2 of Section A”hyperlink 630 Click on “Add Product X to Cart hyperlink” 640 Visitorconfirm purchase of Product X and submits all relevant billing andshipping information 650 No action takenthe following attribution may be inferred:

Metric Product Page Product pur- Rev- Navigation Entity Clicks Viewscartings chases enue Product Detail Pages No Product X Detail Page 0 0 0$0 Product Y Detail Page 0 0 0 $0 Section Pages No Section A: Page 1 1 00 $0 Section A: Page 2 1 1 Prod 1 Prod $1 X X Commerce: Shopping Cart 10 0 $0 page Commerce: “Thank You” 1 0 0 $0 page Sections No Section A 21 Prod 1 Prod $1 X X Commerce Section 2 0 0 $0 Traffic-drivinghyperlinks No 600: Page 2 of Section A 1 1 Prod 1 Prod $1 X X 610: AddProduct X to Cart 0 0 0 $0 620: Add Product Y to Cart 0 0 0 $0 630: AddProduct X to Cart 0 0 0 $0 630: Add Product Y to Cart $0 Producthyperlinks No 600: Product X 0 0 0 $0 600: Product Y 0 0 0 $0 630:Product X (implicit) 0 1 1 $1 630: Product Y (implicit) 0 0 0 $0

According to the following visitor's navigation path:

Page Visitor Action Taken on the Page 600 Click on “Page 2 of Section A”hyperlink 630 Click on “Add Product Y to Cart hyperlink” 640 Visitorconfirm purchase of Product Y and submits all relevant billing andshipping information 650 No action takenthe following attribution may be inferred:

Metric Product Page Product pur- Rev- Navigation Entity Clicks Viewscartings chases enue Product Detail Pages No Product X Detail Page 0 0 0$0 Product Y Detail Page 0 0 0 $0 Section Pages No Section A: Page 1 1 00 $0 Section A: Page 2 1 1 Prod 1 Prod $5 Y Y Commerce: Shopping Cart 10 0 $0 page Commerce: “Thank You” 1 0 0 $0 page Sections No Section A 21 Prod 1 Prod $5 Y Y Commerce Section 2 0 0 $0 Traffic-drivinghyperlinks No 600: Page 2 of Section A 1 1 Prod 1 Prod $5 Y Y 610: AddProduct X to Cart 0 0 0 $0 620: Add Product Y to Cart 0 0 0 $0 630: AddProduct X to Cart 0 0 0 $0 630: Add Product Y to Cart 0 0 0 $0 Producthyperlinks No 600: Product X 0 0 0 $0 600: Product Y 0 0 0 $0 630:Product X (implicit) 0 0 0 $0 630: Product Y (implicit) 0 1 1 $5

Page Pairing Implementation

Once attribution is inferred based on the visitor activity determinedfrom the event level data, analytics system 340 generates resultant datafor providing client reports via a page pairing implementation (step120), according to an example embodiment of the present invention.

For each visitor session in the event level data, every page-by-pagenavigation is broken down. For example, assume the following path forone visit by a visitor on the client's website, which only has oneproduct for sale for $1 each

-   -   Page A>Page B>Page C>Page D>Page B>Page C>Page D>Page        A>Purchased all products in Shopping Cart

Also, for simplicity, assume that all of these pages are section pages(which, as mentioned above, may include a shopping cart page), since aproduct detail page would cause the carting attribution to go backfurther than one page from an “add-to-cart” event.

In order to create a processed transition table to be used for providingclient reports, a raw transition table is first created that holds the 5transitions (i.e., pairings of sequentially viewed pages):

Raw transition table Transition # “From” page “To” Page 1 Page A Page B2 Page B Page C 3 Page C Page D 4 Page D Page B 5 Page B Page C 6 Page CPage D 7 Page D Page A

Any of these transitions can have an event associated with it that iscaptured on the “to” page. These events can be “add-to-cart” events or apurchase event indicating that a product of a given quantity and totalprice is associated.

Raw transition table with event association and Result Transition “From”“To” Product # page Page Event on sale Result 1 Page A Page B Add 1product Yes Purchased to cart product 2 Page B Page C Nothing No None 3Page C Page D Nothing No None 4 Page D Page B Add 1 product YesPurchased to cart product 5 Page B Page C Nothing No None 6 Page C PageD Add 1 product Yes Purchased to cart product 7 Page D Page A Nothing NoNone

The implication of each “add-to-cart” event based on the inferredattribution methodology is to say that the “from” page in an“add-to-cart” transition page is the page where the product is on sale.Note that the inferred attribution process may take place during orbefore the page pairing implementation.

Extra processing may then be performed on this table to include all ofthe attribution and extra data associated with the smallest overallnumber of transitions:

Processed transition table # non-unique # unique “From” “To” sessionsession Product page Page transitions transitions on Sale Result Page APage B 1 1 Yes Product purchased for $1 Page B Page C 2 1 No None Page CPage D 2 1 Yes Product purchased for $1 Page D Page B 1 1 Yes Productpurchased for $1 Page D Page A 1 1 No None

The processed transition table may be stored in database 370, and thefollowing reports may use information from this table to provide clientreports (step 130).

In order to generate a report that displays the visitor's navigationpath through a specified page of the client's website, the followingrelevant data from the processed transition table is used:

# unique session “From” page “To” Page transitions Page A Page B 1 PageB Page C 1 Page C Page D 1 Page D Page B 1 Page D Page A 1

Note since two transitions (Page B to Page C and from page C to Page D)happen twice in the same session, it is only counted once for thisparticular report.

To create this the left hand side of this report, the “to” page isfiltered for Page B only and the following is extracted from the “from”pages. The results are as follows:

# unique session “From” page “To” Page transitions Page A Page B 1 PageD Page B 1

To create the right hand side of this report, the “from” page isfiltered by Page B only and the following is extracted from the “to”pages. The results are as follows:

# unique session “From” page “To” Page transitions Page B Page C 1

The output for the visitor's navigation path report is as follows:

“From” Page # sessions “To” page # sessions Page A 1 Page B Page C 1Page D 1

FIG. 7 shows a navigation path report using data representing more thanone visitor and session.

In order to generate a report that displays the visitor's shoppingactivity generated by links clicked at least once on a specified webpage (i.e., page link performance), the following relevant data from theprocessed transition table is used:

# non-unique “From” “To” session Product page Page transitions on SaleResult Page A Page B 1 Yes Product purchased for $1 Page B Page C 2 NoNone Page C Page D 2 Yes Product purchased for $1 Page D Page B 1 YesProduct purchased for $1 Page D Page A 1 No None

To generate this report, the “from” page is held constant to determinewhat has happened from this page. Filtering for “from” pages for Page Dand all of the locations where the hyperlinks take the individual, theresulting data is extracted

# non-unique “From” “To” session Product page Page transitions on SaleResult Page D Page B 1 Yes Product purchased for $1 Page D Page A 1 NoNone Page A Page B 1 Yes Product purchased for $1 Page B Page C 2 NoNone

“From” Page A has revenue associated with it, therefore the“traffic-driving” link to Page A has 1 carted item, 1 purchased item andRevenue. Since the “from” Page B has no attributable metrics to it,there is no change in the metrics.

The output for the page link performance report is as follows:

Carted Purchased Link Type Item Clicks Items Items Revenue ProductProduct 0 1 1 $1 Traffic-driving Page A 1 1 1 $1

Note that if:

-   -   there were any product detail pages for the product that had a        hyperlink to that page from Page D, those product detail page        loads would be counted as “Clicks” in this table    -   there were no purchases made from Page A, the number of Cart        Items, Purchased Items and Revenue would all be zero in value

FIG. 8 shows a page link performance report using data representing morethan one visitor and session.

In order to generate a report that displays all of the web pages where aspecified product is displayed (i.e., product placement), the followingrelevant data from the processed transition table is used:

# non-unique “From” page session transitions Result Page A 1 Productpurchased for $1 Page B 2 None Page C 2 Product purchased for $1 Page D1 Product purchased for $1 Page D 1 None

To generate this report, filter all “from” pages by whether a productwas purchased from that page and sum across all “From” pages:

# non-unique “From” page session transitions Result Page A 1 Productpurchased for $1 Page C 2 Product purchased for $1 Page D 1 Productpurchased for $1

The output for the product placement report is as follows:

Page Page Views Purchase Items Revenue Page A 1 1 $1 Page C 2 1 $1 PageD 1 1 $1

Note that there are no “click” counts in this table—just the number oftimes the “from” page has been loaded.

FIG. 9 shows a product placement report using data representing morethan one visitor and session.

Several embodiments of the invention are specifically illustrated and/ordescribed herein. However, it will be appreciated that modifications andvariations of the invention are covered by the above teachings andwithin the purview of the appended claims without departing from thespirit and intended scope of the invention.

For example, embodiments of the invention can be applied tonon-merchandising websites by capturing the metrics mapped to thenavigation entities. Publishers interested in determining which ad spaceis valuable can use such metrics as ad exposures, advertiser hyperlinkclicks, and website registration. Non-publishers interested indetermining what applications and documents are accessed can use suchmetrics as application and documentation downloads.

Also, this invention can be applied to multiple client websites withdistinct URLs by collating their respective data under one client asrecognized by the system. By defining either individual sections foreach distinct URL as a separate section or by defining the entirewebsite entities as separate sections, the inferred attribution ofmetrics would apply in a similar fashion as applied by the single clientwebsite embodiment described herein.

Additionally, with a number of clients with similar application of thesystem (e.g., selling furniture online, newspaper publishing website,etc.), reports can be provided to compare one client's metrics againstan anonymous pool of other clients to determine its relative standing inthe industry on several metrics.

1. A method comprising: receiving event-level data representing visitoractivity through navigation entities on a website; parsing, by one ormore computers, contents of the visitor activity; and inferring, basedon the parsing and by the one or more computers, attribution of one ormore metrics to at least one navigation entity, with inferring beingindependent of explicit attribution information in the event-level data.2. The method of claim 1, further comprising: attributing the one ormore metrics to the at least one navigation entity.
 3. The method ofclaim 1, wherein at least one of the navigation entities precedesanother of the navigation entities in a navigation path of a visitorassociated with the visitor activity.
 4. The method of claim 1, whereinthe navigation entities include web pages provided by the website. 5.The method of claim 1, wherein at least one of the one or more metricsincludes a metric indicative of whether a particular type of hyperlinkis provided in at least one of the navigation entities.
 6. The method ofclaim 5, wherein the particular type of hyperlink includes atraffic-driving hyperlink.
 7. A system comprising: one or moreprocessing devices; and one or more machine-readable media storinginstructions that are executable by the one or more processing devicesto perform operations comprising: receiving event-level datarepresenting visitor activity through navigation entities on a website;parsing, by one or more computers, contents of the visitor activity; andinferring, based on the parsing and by the one or more computers,attribution of one or more metrics to at least one navigation entity,with inferring being independent of explicit attribution information inthe event-level data.
 8. The system of claim 7, wherein the operationsfurther comprise: attributing the one or more metrics to the at leastone navigation entity.
 9. The system of claim 7, wherein at least one ofthe navigation entities precedes another of the navigation entities in anavigation path of a visitor associated with the visitor activity. 10.The system of claim 7, wherein the navigation entities include web pagesprovided by the website.
 11. The system of claim 7, wherein at least oneof the one or more metrics includes a metric indicative of whether aparticular type of hyperlink is provided in at least one of thenavigation entities.
 12. The system of claim 11, wherein the particulartype of hyperlink includes a traffic-driving hyperlink.
 13. One or moremachine-readable media storing instructions that are executable by theone or more processing devices to perform operations comprising:receiving event-level data representing visitor activity throughnavigation entities on a website; parsing, by one or more computers,contents of the visitor activity; and inferring, based on the parsingand by the one or more computers, attribution of one or more metrics toat least one navigation entity, with inferring being independent ofexplicit attribution information in the event-level data.
 14. The one ormore machine-readable media of claim 13, wherein the operations furthercomprise: attributing the one or more metrics to the at least onenavigation entity.
 15. The one or more machine-readable media of claim13, wherein at least one of the navigation entities precedes another ofthe navigation entities in a navigation path of a visitor associatedwith the visitor activity.
 16. The one or more machine-readable media ofclaim 13, wherein the navigation entities include web pages provided bythe website.
 17. The one or more machine-readable media of claim 13,wherein at least one of the one or more metrics includes a metricindicative of whether a particular type of hyperlink is provided in atleast one of the navigation entities.
 18. The one or moremachine-readable media of claim 17, wherein the particular type ofhyperlink includes a traffic-driving hyperlink.